T-Drive: Driving Directions Based on Taxi Trajectories

Jing Yuan, Yu Zheng, Chengyang Zhang, Wenlei Xie, Xing Xie, Guangzhong Sun, Yan Huang

Proceedings of 18th ACM SIGSPATIAL Conference on Advances in Geographical Information Systems |

Published by ACM SIGSPATIAL GIS 2010

Best Paper Award

View Publication

GPS-equipped taxis can be regarded as mobile sensors probing traffic flows on road surfaces, and taxi drivers are usually experienced in finding the fastest (quickest) route to a destination based on their knowledge. In this paper, we mine smart driving directions from the historical GPS trajectories of a large number of taxis, and provide a user with the practically fastest route to a given destination at a given departure time. In our approach, we propose a time-dependent landmark graph, where a node (landmark) is a road segment frequently traversed by taxis, to model the intelligence of taxi drivers and the properties of dynamic road networks. Then, a Variance-Entropy-Based Clustering approach is devised to estimate the distribution of travel time between two landmarks in different time slots. Based on this graph, we design a two-stage routing algorithm to compute the practically fastest route. We build our system based on a real-world trajectory dataset generated by over 33,000 taxis in a period of 3 months, and evaluate the system by conducting both synthetic experiments and in-the-field evaluations. As a result, 60-70% of the routes suggested by our method are faster than the competing methods, and 20% of the routes share the same results. On average, 50% of our routes are at least 20% faster than the competing approaches.

Download the Trajectory Data

Urban Computing with Taxicabs

This video showcases three application scenarios that have been enabled in the urban computing project. 1) Finding smart driving direction based on taxi trajectories; 2) A passenger-cabbie recommender system; 3) Glean the flawed urban planning in terms of people's city-wide mobility patterns learned from taxi trajectories. Contact: Yu Zheng, Researcher at Microsoft Research Asia, yuzheng@microsoft.com [video width="854" height="480" mp4="https://www.microsoft.com/en-us/research/wp-content/uploads/2011/10/urban_planning_Ubicomp2011_yuzheng-2.mp4"][/video]